Adaptive fitness training

ABSTRACT

Streaming real-time fitness activity for analysis to determine if the user should change the exercise type, the wait duration between exercises, or the intensity level to increase the effectiveness level of the current activity for defined fitness goals and objectives of the user and notifying the user of the changes to the fitness activity in real-time while performing the activity. Evaluating the fitness goals of the user to define a fitness plan with specific exercises based on the fitness programs ranking learned from social media sentiment and proven results based on real-time feedback results of the user correlated to the activity. The fitness plan is evaluated based on current measurements of progress with goals to recommend and forecast changes to the fitness plan.

BACKGROUND OF THE INVENTION

The present invention relates generally to the field of physical fitness, and more particularly to fitness training.

Changing exercise routine and intensity for both strength training and cardiovascular exercise has an added benefit of accelerated fat burning during the same time period as compared to standard low intensity exercises. Activity tracking devices monitor many exercise characteristics, such as steps, distance, heart rate, and estimated calories burned.

SUMMARY

According to an aspect of the present invention, there is a computer-implemented method, computer program product, and/or system that perform(s) the following steps (not necessarily in the following order): (i) receive fitness activity data from one or more sensors, wherein the fitness activity data describes a fitness activity performed by a user; (ii) determine target values for the fitness activity data, wherein the target values increase an effectiveness level of the fitness activity; (iii) determine a change to the fitness activity, wherein the change is based on the fitness activity data and the target values; and (iv) report the change to the user.

According to an aspect of the present invention, there is a computer-implemented method, computer program product, and/or system to analyze fitness procedures that perform(s) the following steps (not necessarily in the following order): (i) receive input to capture fitness and wellness goal of a user; (ii) receive input to capture data from at least one wearable of the user, such as a smartwatch with sensors that is used to capture real-time data and other user information, such as weight; (iii) receive input to capture external information, such as studies and social media, to gather information about fitness and wellness; (iv) storing listing of fitness and wellness resources available to the user (v) storing captured data; (vi) analyzing external information with respect to the goal and real-time data of the user and to other data and resources to recommend activity types, wait times between activities, and intensity for activity.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram view of a first embodiment of a system according to the present invention.

FIG. 2 is a flowchart showing a first embodiment method performed, at least in part, by the first embodiment system.

FIG. 3 is a block diagram view of a machine logic (for example, software) portion of the first embodiment system.

FIG. 4 is a flowchart showing a second embodiment method.

FIG. 5 is a block diagram view of a machine logic (for example, software) portion of a second embodiment system.

DETAILED DESCRIPTION

Streaming real-time fitness activity data for analysis to determine if the user should change the exercise type, the wait duration between exercises, or the intensity level to increase the effectiveness level of the current activity for defined fitness goals and objectives of the user and notifying the user of the changes to the fitness activity in real-time while performing the activity. Evaluating the fitness goals of the user to define a fitness plan with specific exercises based on the fitness programs ranking learned from social media sentiment and proven results based on real-time feedback results of the user correlated to the activity. The fitness plan is evaluated based on current measurements of progress with goals to recommend and forecast changes to the fitness plan.

This Detailed Description section is divided into the following sub-sections: (i) The Hardware and Software Environment; (ii) Example Embodiment; (iii) Further Comments and/or Embodiments; and (iv) Definitions.

I. The Hardware and Software Environment

The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.

Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.

These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

An embodiment of a possible hardware and software environment for software and/or methods according to the present invention will now be described in detail with reference to the Figures. FIG. 1 is a functional block diagram illustrating various portions of networked computers system 100, including: computing sub-system 102; client sub-systems 104, 106, 108, 110, 112; communication network 114; computing device 200; communication unit 202; processor set 204; input/output (I/O) interface set 206; memory device 208; persistent storage device 210; display device 212; external device set 214; random access memory (RAM) devices 230; cache memory device 232; and program 300.

Client sub-systems 104, 106, 108, 110, 112 represent activity tracking devices. Client sub-systems 104, 106, 108, 110, 112 may be mobile devices, smart phones, wearable devices, exercise equipment, or monitors connected to exercise equipment. Client sub-systems 104, 106, 108, 110, 112 may include a feedback device, such as a haptic feedback device, speaker, or display.

Computing sub-system 102 is, in many respects, representative of the various computer sub-system(s) in the present invention. Accordingly, several portions of sub-system 102 will now be discussed in the following paragraphs.

Sub-system 102 may be a laptop computer, tablet computer, netbook computer, personal computer (PC), a desktop computer, a personal digital assistant (PDA), a smart phone, or any programmable electronic device capable of communicating with the client sub-systems via network 114. Program 300 is a collection of machine readable instructions and/or data that is used to create, manage and control certain software functions that will be discussed in detail, below, in the Example Embodiment sub-section of this Detailed Description section.

Sub-system 102 is capable of communicating with other computer sub-systems via network 114. Network 114 can be, for example, a local area network (LAN), a wide area network (WAN) such as the Internet, or a combination of the two, and can include wired, wireless, or fiber optic connections. In general, network 114 can be any combination of connections and protocols that will support communications between server and client sub-systems.

Sub-system 102 is shown as a block diagram with many double arrows. These double arrows (no separate reference numerals) represent a communications fabric, which provides communications between various components of sub-system 102. This communications fabric can be implemented with any architecture designed for passing data and/or control information between processors (such as microprocessors, communications and network processors, etc.), system memory, peripheral devices, and any other hardware components within a system. For example, the communications fabric can be implemented, at least in part, with one or more buses.

Memory 208 and persistent storage 210 are computer-readable storage media. In general, memory 208 can include any suitable volatile or non-volatile computer-readable storage media. It is further noted that, now and/or in the near future: (i) external device(s) 214 may be able to supply, some or all, memory for sub-system 102; and/or (ii) devices external to sub-system 102 may be able to provide memory for sub-system 102.

Program 300 is stored in persistent storage 210 for access and/or execution by one or more of the respective computer processors 204, usually through one or more memories of memory 208. Persistent storage 210: (i) is at least more persistent than a signal in transit; (ii) stores the program (including its soft logic and/or data), on a tangible medium (such as magnetic or optical domains); and (iii) is substantially less persistent than permanent storage. Alternatively, data storage may be more persistent and/or permanent than the type of storage provided by persistent storage 210.

Program 300 may include both machine readable and performable instructions and/or substantive data (that is, the type of data stored in a database). In this particular embodiment, persistent storage 210 includes a magnetic hard disk drive. To name some possible variations, persistent storage 210 may include a solid state hard drive, a semiconductor storage device, read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, or any other computer-readable storage media that is capable of storing program instructions or digital information.

The media used by persistent storage 210 may also be removable. For example, a removable hard drive may be used for persistent storage 210. Other examples include optical and magnetic disks, thumb drives, and smart cards that are inserted into a drive for transfer onto another computer-readable storage medium that is also part of persistent storage 210.

Communications unit 202, in these examples, provides for communications with other data processing systems or devices external to sub-system 102. In these examples, communications unit 202 includes one or more network interface cards. Communications unit 202 may provide communications through the use of either or both physical and wireless communications links. Any software modules discussed herein may be downloaded to a persistent storage device (such as persistent storage device 210) through a communications unit (such as communications unit 202).

I/O interface set 206 allows for input and output of data with other devices that may be connected locally in data communication with computer 200. For example, I/O interface set 206 provides a connection to external device set 214. External device set 214 will typically include devices such as a keyboard, keypad, a touch screen, and/or some other suitable input device. External device set 214 can also include portable computer-readable storage media such as, for example, thumb drives, portable optical or magnetic disks, and memory cards. Software and data used to practice embodiments of the present invention, for example, program 300, can be stored on such portable computer-readable storage media. In these embodiments the relevant software may (or may not) be loaded, in whole or in part, onto persistent storage device 210 via I/O interface set 206. I/O interface set 206 also connects in data communication with display device 212.

Display device 212 provides a mechanism to display data to a user and may be, for example, a computer monitor or a smart phone display screen.

Sensor device 216 provides a mechanism to capture data describing the physical activity of a user and equipment used in the activity. For example, sensor device 216 may include sensors to capture the steps, distance, duration, and/or heart rate of the user.

Feedback device 218 provides a mechanism to output feedback to a user. For example, feedback device 218 may vibrate, provide a haptic cue, play an audio cue, or display a visual cue to output feedback to a user.

The programs described herein are identified based upon the application for which they are implemented in a specific embodiment of the invention. However, it should be appreciated that any particular program nomenclature herein is used merely for convenience, and thus the invention should not be limited to use solely in any specific application identified and/or implied by such nomenclature.

The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the invention. The terminology used herein was chosen to best explain the principles of the embodiment, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

II. Example Embodiment

Program 300 operates to receive real-time activity data for analysis to determine if the user should change the exercise type, wait duration between exercises, or intensity level to optimize the effectiveness (or increase an effectiveness level) of the current activity for fitness goals and objectives of the user. Program 300 notifies the user of the changes while the user is engaged in the activity. Program 300 recommends changes to future activities in the fitness plan and changes to the fitness plan, and program 300 forecasts the results of the changes.

FIG. 2 shows flowchart 250 depicting a method according to the present invention. FIG. 3 shows program 300 for performing at least some of the method steps of flowchart 250. This method and associated software will now be discussed, over the course of the following paragraphs, with extensive reference to FIG. 2 (for the method step blocks) and FIG. 3 (for the software blocks).

Processing begins at step S255, where goal module (“mod”) 305 receives fitness goals of a user. For example, goal mod 305 may receive a goal to lose weight or a goal to increase strength. In this embodiment, goal mod 305 receives a current fitness condition of the user and a goal fitness condition with measurable indicators, which measure the progress of the user. For example, goal mod 305 may receive a starting weight and a goal weight of the user. In this example, goal mod 305 receives intermediate weights of the user, which indicate progress towards the goal weight. In another example, goal mod 305 receives a current repetition and weight ability of the user and receive a target repetition and weight.

Processing proceeds to step S260, where plan mod 310 recommends a fitness plan to the user. In this embodiment, plan mod 310 searches online resources for fitness plans based on a defined type, objective, and social sentiment. Alternatively, a fitness advisor suggests fitness plans to the user. In this embodiment, plan mod 310 utilizes historic activity data correlated with an effectiveness level to rank fitness plans based on likely result. In this embodiment, plan mod 310 recommends a fitness plan based on the ranking. Alternatively, a fitness advisor specifies a fitness plan for the user.

Processing proceeds to step S265, where activity mod 315 receives fitness activity data. In this embodiment, activity mod 315 receives exercise type, intensity, and duration data, while the user is engaged in the activity. For example, activity mod 315 receives number of steps taken, distance, floors ascended/descended, and date and time data. In another example, activity mod 315 receives sensor data from a wearable device, including heart rate and/or motion data.

Processing proceeds to step S270, where analysis mod 320 analyzes the fitness activity data. In this embodiment, analysis mod 320 determines characteristics of the activity data, such as activity type, activity duration, wait time duration, fitness equipment data, and/or fitness intensity. In this embodiment, analysis mod 320 analyzes the activity data by comparing the fitness goals and current progress with initial fitness data and a pre-defined progress timeline. In this embodiment, analysis mod 320 determines target values, also referred to as optimized values, based on historic activity data, and analysis mod 320 compares the target values to the current activity data to determine the effectiveness level of the current activity.

Processing proceeds to step S275, where recommendation mod 325 recommends changes to the fitness activity. In this embodiment, recommendation mod 325 recommends changes to the current fitness activity, while the user is engaged in the activity, to increase the effectiveness level of the activity based on the optimized values. For example, recommendation mod 325 may recommend an increase/decrease in intensity, a shortening/lengthening of rest times between exercises, or a change of activity type based on the determined effectiveness level of the current activity in step S270.

Processing proceeds to step S280, where forecast mod 330 recommends changes to the fitness plan and forecasts likely results. In this embodiment, forecast mod 330 compares measurable indicators of the current activity to initial fitness data to determine the effectiveness level of the current activity. In this embodiment, forecast mod 330 forecasts the likely most effective changes to the fitness plan based on determined effectiveness level of activities during a pre-defined period. For example, forecast mod 330 may recommend changes to future activities in the fitness plan, or forecast mod 330 may recommend alternative activities to be completed as part of a modified fitness plan.

III. Further Comments and/or Embodiments

Some embodiments of the present invention recognize the following facts, potential problems and/or potential areas for improvement with respect to the current state of the art: (i) access to fitness trainers may be unavailable and/or costly; (ii) fitness activity tracking without feedback may not enable a user to maximize fitness gains; and/or (iii) activity history may not correspond with progress towards a fitness goal.

Some embodiments of the present invention provide real-time adjustments to fitness activities to optimize training time and maximize fitness gains. Some embodiments of the present invention provide adjustments to future activities in a fitness plan to optimize progress towards a fitness goal. Some embodiments of the present invention provide replacement activities in a fitness plan to optimize progress towards a fitness goal.

Further embodiments of the present invention are discussed in the paragraphs that follow with reference to FIGS. 4 and 5.

FIG. 4 shows flowchart 400 depicting a second embodiment method, performed on a system (not shown) similar to networked computer system 100, according to the present invention. FIG. 5 shows program 500 for performing at least some of the method steps of flowchart 400. This method will now be discussed, over the course of the following paragraphs, with extensive reference to FIG. 4 (for the method step blocks) and FIG. 5 (for the software blocks).

Processing begins at step S410, where configuration mod 505 determines that prerequisites are satisfied and receives a pre-configuration. In this embodiment, configuration mod 505 determines that a wearable device with activity tracking capability is connected and worn by a user. In this embodiment, configuration mod 505 receives a user profile corresponding with program 500.

Processing proceeds to step S420, where goal mod 510 receives a fitness objective and goals. In this embodiment, goal mod 510 receives a current fitness condition and measurable indicators to monitor progress and effectiveness. For example, goal mod 510 may receive a fitness objective to lose weight from a user. In this example, goal mod 510 may receive a starting weight of the user and a goal weight, which the user wishes to achieve. In another example, goal mod 510 may receive a fitness objective to strength train from a user. In this example, goal mod 510 may receive a current strength ability and a goal strength ability. In this embodiment, goal mod 510 receives a list of activities to which the user has access. For example, goal mod 510 may receive a list that the user has access to a gym, is able to hike, is able to run, and/or has access to a pool for swimming.

Processing proceeds to step S430, where plan mod 515 recommends a fitness plan to the user. In this embodiment, plan mod 515 searches online resources for fitness plans based on a defined type, objective, and social sentiment. In this embodiment, plan mod 515 utilizes historic activity data correlated with effectiveness to rank fitness plans based on result. In this embodiment, plan mod 515 recommends a fitness plan based on the ranking.

Processing proceeds to step S440, where activity mod 520 captures wearable activity data. In this embodiment, activity mod 520 receives activity data from sensors in a wearable device worn by the user. For example, activity mod 520 may receive data including exercise type, steps, distance, floors ascended, date and time data and biometric data, such as heart rate. In this embodiment, activity mod 520 receives data from sensors in fitness equipment. For example, activity mod 520 may receive weight load data.

Processing proceeds to step S450, where analysis mod 525 analyzes real-time fitness activity data. In this embodiment, analysis mod 525 determines characteristics of the activity data, such as activity type, activity duration, wait time duration, fitness equipment data, and/or fitness intensity. In this embodiment, analysis mod 525 analyzes the activity data by comparing the fitness goals and current progress with initial fitness data and a pre-defined progress timeline. In this embodiment, analysis mod 525 determines optimized values based on historic activity data, and analysis mod 525 compares the optimized values to the current activity data to determine the effectiveness of the current activity.

Processing proceeds to step S460, where determination mod 530 determines whether to recommend changes. In this embodiment, determination mod 530 determines whether to recommend changes by comparing current activity data to the optimized values. In some embodiments, determination mod 530 determines whether to recommend changes based on a variation from optimized values exceeding a pre-determine threshold.

Responsive to a determination to recommend changes (“YES” branch, S460), processing proceeds to step S470, where recommendation mod 535 alerts the user of recommended fitness changes. In the embodiment, recommendation mod 535 alerts the user of changes to the current activity to increase the effectiveness of the activity based on the optimized values. For example, recommendation mod 535 may recommend changes of activity type, wait time between exercises, and/or activity intensity.

Processing proceeds to step S480, where forecast mod 540 forecasts changes and results to the fitness plan. In this embodiment, forecast mod 540 compares measurable indicators of the current activity to initial fitness data to determine the effectiveness of the current activity. In this embodiment, forecast mod 540 forecasts the likely most effective changes to the fitness plan based on effectiveness of activities during a pre-defined period.

Some embodiments of the present invention may include one, or more, of the following features, characteristics, and/or advantages: (i) real-time adjustments to fitness activities; (ii) adjustments based on historic activity data; (iii) adjustments to future activities in a fitness plan; (iv) replacement activities in a fitness plan; (v) adjustments to a fitness plan; (vi) recommending alternative fitness plans; (vii) nutrition tracking; (viii) nutrition adjustments; and (ix) social media integration.

IV. Definitions

Present invention: should not be taken as an absolute indication that the subject matter described by the term “present invention” is covered by either the claims as they are filed, or by the claims that may eventually issue after patent prosecution; while the term “present invention” is used to help the reader to get a general feel for which disclosures herein that are believed as maybe being new, this understanding, as indicated by use of the term “present invention,” is tentative and provisional and subject to change over the course of patent prosecution as relevant information is developed and as the claims are potentially amended.

Embodiment: see definition of “present invention” above—similar cautions apply to the term “embodiment.”

and/or: inclusive or; for example, A, B “and/or” C means that at least one of A or B or C is true and applicable.

User/subscriber: includes, but is not necessarily limited to, the following: (i) a single individual human; (ii) an artificial intelligence entity with sufficient intelligence to act as a user or subscriber; and/or (iii) a group of related users or subscribers.

Module/Sub-Module: any set of hardware, firmware and/or software that operatively works to do some kind of function, without regard to whether the module is: (i) in a single local proximity; (ii) distributed over a wide area; (iii) in a single proximity within a larger piece of software code; (iv) located within a single piece of software code; (v) located in a single storage device, memory or medium; (vi) mechanically connected; (vii) electrically connected; and/or (viii) connected in data communication.

Computer: any device with significant data processing and/or machine readable instruction reading capabilities including, but not limited to: desktop computers, mainframe computers, laptop computers, field-programmable gate array (FPGA) based devices, smart phones, personal digital assistants (PDAs), body-mounted or inserted computers, embedded device style computers, application-specific integrated circuit (ASIC) based devices.

The term “real time” (and the adjective “real-time”) includes any time frame of sufficiently short duration as to provide reasonable response time for information processing as described. Additionally, the term “real time” (and the adjective “real-time”) includes what is commonly termed “near real time,” generally any time frame of sufficiently short duration as to provide reasonable response time for on-demand information processing as described (e.g., within a portion of a second or within a few seconds). These terms, while difficult to precisely define, are well understood by those skilled in the art. 

What is claimed is:
 1. A computer-implemented method comprising: receiving fitness activity data from one or more sensors, wherein the fitness activity data describes a fitness activity performed by a user; determining target values for the fitness activity data, wherein the target values increase an effectiveness level of the fitness activity; determining a change to the fitness activity based on the fitness activity data and the target values; and reporting the change to the fitness activity to the user.
 2. The computer-implemented method of claim 1, further comprising: receiving one or more fitness goals of the user, wherein the target values are based on the one or more fitness goals.
 3. The computer-implemented method of claim 2, further comprising: recommending a fitness plan to the user, wherein the fitness plan is based on the one or more fitness goals and a current fitness condition of the user.
 4. The computer-implemented method of claim 3, further comprising: determining a change to the fitness plan based on at least one of: the one or more fitness goals, the current fitness condition of the user, the fitness activity data, the target values, and forecasted results of the change to the fitness plan.
 5. The computer-implemented method of claim 1, wherein determining the target values, further comprises: receiving historic fitness activity data, including activity characteristics including at least one of activity type, intensity, duration, and rest period duration; determining an effectiveness level of the historic fitness activity data based on a fitness condition of the user; and determining the target values based on the activity characteristics corresponding to the effectiveness level.
 6. The computer-implemented method of claim 1, wherein determining the change to the fitness activity, further comprises: determining whether the fitness activity data is within a threshold of the target values; and responsive to a determination that the fitness activity data is not within the threshold of the target values, selecting the change to the fitness activity from at least one of increased intensity, increased duration, decreased rest period duration, and alternative activity. 